# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Miku_PP_v3.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy, merge_informative_pair from pandas import DataFrame import talib.abstract as ta import logging import freqtrade.vendor.qtpylib.indicators as qtpylib # -------------------------------- import pandas as pd import numpy as np import technical.indicators as ftt from freqtrade.exchange import timeframe_to_minutes from technical.util import resample_to_interval, resampled_merge logger = logging.getLogger(__name__) def pivots_points(dataframe: pd.DataFrame, timeperiod=1, levels=4) -> pd.DataFrame: """ Pivots Points https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/ Formula: Pivot = (Previous High + Previous Low + Previous Close)/3 Resistance #1 = (2 x Pivot) - Previous Low Support #1 = (2 x Pivot) - Previous High Resistance #2 = (Pivot - Support #1) + Resistance #1 Support #2 = Pivot - (Resistance #1 - Support #1) Resistance #3 = (Pivot - Support #2) + Resistance #2 Support #3 = Pivot - (Resistance #2 - Support #2) ... :param dataframe: :param timeperiod: Period to compare (in ticker) :param levels: Num of support/resistance desired :return: dataframe """ data = {} low = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index, data=dataframe['low']), window=timeperiod) high = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index, data=dataframe['high']), window=timeperiod) # Pivot data['pivot'] = qtpylib.rolling_mean(series=qtpylib.typical_price(dataframe), window=timeperiod) # Resistance #1 # data["r1"] = (2 * data["pivot"]) - low ... Standard # R1 = PP + 0.382 * (HIGHprev - LOWprev) ... fibonacci data['r1'] = data['pivot'] + 0.382 * (high - low) data['rS1'] = data['pivot'] + 0.0955 * (high - low) # Resistance #2 # data["s1"] = (2 * data["pivot"]) - high ... Standard # S1 = PP - 0.382 * (HIGHprev - LOWprev) ... fibonacci data['s1'] = data['pivot'] - 0.382 * (high - low) # Calculate Resistances and Supports >1 for i in range(2, levels + 1): prev_support = data['s' + str(i - 1)] prev_resistance = data['r' + str(i - 1)] # Resitance data['r' + str(i)] = data['pivot'] - prev_support + prev_resistance # Support data['s' + str(i)] = data['pivot'] - (prev_resistance - prev_support) return pd.DataFrame(index=dataframe.index, data=data) def create_ichimoku(dataframe, conversion_line_period, displacement, base_line_periods, laggin_span): ichimoku = ftt.ichimoku(dataframe, conversion_line_period=conversion_line_period, base_line_periods=base_line_periods, laggin_span=laggin_span, displacement=displacement) dataframe[f'tenkan_sen_{conversion_line_period}'] = ichimoku['tenkan_sen'] dataframe[f'kijun_sen_{conversion_line_period}'] = ichimoku['kijun_sen'] dataframe[f'senkou_a_{conversion_line_period}'] = ichimoku['senkou_span_a'] dataframe[f'senkou_b_{conversion_line_period}'] = ichimoku['senkou_span_b'] class Github_DerSalvador_freqtrade_helm_chart__Miku_PP_v3__20260115_122204(IStrategy): INTERFACE_VERSION = 3 '\n Github_DerSalvador_freqtrade_helm_chart__Miku_PP_v3__20260115_122204\n La base de la Estrategia es: Miku_PP_v2 y Miku_1m_5m_CSen44_1_5m\n \n Provando en:\n Miku_1m_5m_CSen444v2_N_1_5\n SymphonIK\n ' # Optimal timeframe for the strategy timeframe = '5m' # generate signals from the 1h timeframe informative_timeframe = '1d' # WARNING: ichimoku is a long indicator, if you remove or use a # shorter startup_candle_count your results will be unstable/invalid # for up to a week from the start of your backtest or dry/live run # (180 candles = 7.5 days) startup_candle_count = 444 # MAXIMUM ICHIMOKU # NOTE: this strat only uses candle information, so processing between # new candles is a waste of resources as nothing will change process_only_new_candles = True minimal_roi = {'0': 10} plot_config = {'main_plot': {'pivot_1d': {}, 'rS1_1d': {}, 'r1_1d': {}, 's1_1d': {}, 'senkou_b_88': {}}, 'subplots': {'MACD': {'macd_1h': {'color': 'blue'}, 'macdsignal_1h': {'color': 'orange'}}}} # WARNING setting a stoploss for this strategy doesn't make much sense, as it will entry # back into the trend at the next available opportunity, unless the trend has ended, # in which case it would exit anyway. # Stoploss: stoploss = -0.1 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] if self.dp: for pair in pairs: informative_pairs += [(pair, '1d')] return informative_pairs def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ # dataframe "1d" """ dataframe1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d') # Pivots Points pp = pivots_points(dataframe1d) dataframe1d['pivot'] = pp['pivot'] dataframe1d['r1'] = pp['r1'] dataframe1d['s1'] = pp['s1'] dataframe1d['rS1'] = pp['rS1'] # Pivots Points dataframe = merge_informative_pair(dataframe, dataframe1d, self.timeframe, '1d', ffill=True) '\n # dataframe normal\n ' '\n create_ichimoku(dataframe, conversion_line_period=9, \n displacement=26, base_line_periods=26, laggin_span=52)\n ' create_ichimoku(dataframe, conversion_line_period=20, displacement=88, base_line_periods=88, laggin_span=88) create_ichimoku(dataframe, conversion_line_period=88, displacement=444, base_line_periods=88, laggin_span=88) create_ichimoku(dataframe, conversion_line_period=355, displacement=880, base_line_periods=175, laggin_span=175) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) "\n Notes: Start Trading\n\n * En 1m\n dataframe['ichimoku_ok'] = (\n (dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) &\n (dataframe['senkou_a_100'] > dataframe['senkou_b_100']) &\n (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &\n (dataframe['kijun_sen_20'] > dataframe['tenkan_sen_444']) &\n (dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &\n (dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &\n (dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &\n (dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])\n ).astype('int')\n\n * En 5m\n dataframe['ichimoku_ok'] = (\n (dataframe['close'] > dataframe['pivot_1d']) &\n (dataframe['r1_1d'] > dataframe['close']) &\n (dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) &\n (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &\n (dataframe['kijun_sen_20'] > dataframe['tenkan_sen_88']) &\n (dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &\n (dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &\n (dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &\n (dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])\n ).astype('int')\n\n\n (dataframe['pivot_1d'] > dataframe['ema20_5m']) anulo ema20_5m para ver si hace entradas en Dry Run\n\n dataframe['trending_over'] = (\n (\n (dataframe['senkou_b_444'] > dataframe['close'])\n )\n |\n (\n (dataframe['pivot_1d'] > dataframe['close'])\n )\n \n ).astype('int')\n\n return dataframe\n " # Start Trading dataframe['pivots_ok'] = ((dataframe['close'] > dataframe['pivot_1d']) & (dataframe['rS1_1d'] > dataframe['close']) & (dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) & (dataframe['senkou_a_20'] > dataframe['senkou_b_20'])).astype('int') dataframe['trending_over'] = (dataframe['senkou_b_88'] > dataframe['close']).astype('int') return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.slow_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[dataframe['pivots_ok'] > 0, 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[dataframe['trending_over'] > 0, 'exit_long'] = 1 return dataframe